Adjusting Image Size with Padding
In CNNs, padding refers to the process of inserting additional pixels at the edges of the input image during convolution operations.
By utilizing padding, the image size can be maintained or adjusted after convolution, helping to ensure that valuable information is not lost even as the network depth increases.
Why Padding is Necessary
Convolution operations cause a reduction in output size as filters move over the image.
Particularly when processing small images, repeated operations can quickly decrease size, risking the loss of crucial information.
Using padding can effectively address these issues, providing benefits such as:
-
Preserving the original image size to retain information in deep networks.
-
Allowing edge pixels to be included in the convolution operation, enabling better feature representation.
Types of Padding
In CNNs, the most common type of padding is Same Padding, where 0 values are added to the edges.
Same Padding helps smaller images retain features adequately, and preserves image information even as the model's depth increases.
For small images, maintaining size using Same Padding is typical, while for larger images, Valid Padding may be used to reduce computation.
In the next lesson, we will explore pooling, a technique often employed in conjunction with padding.
Want to learn more?
Join CodeFriends Plus membership or enroll in a course to start your journey.